Modeling Location Choice of Taxi Drivers for Passenger Pickup Using GPS Data

Abstract

Recently the traditional taxi industry is struggling to keep its market share, especially with the emergence of new transport network companies (e.g., Uber). One of the problems with the traditional taxi services is the difficulty of matching the taxi demand to its supply when there is no phone booking or another reservation system. In that perspective, the taxi driver's experience is important in reaching the next passenger. A taxi driver with limited experience may not know the high-demand locations and times of taxi stands or street sections to visit after dropping off a passenger. This causes a large number of vacant taxi drivers to regularly cruise the roads to search a passenger, contributing to congestion, pollution, and resource waste. We formulate the problem of a taxi driver's next passenger pickup location as a destination choice problem. Vacant taxi trips between drop-off and pickup points are extracted from GPS records obtained from a taxi operator in Lisbon, Portugal to understand the travel behavior of vacant taxi drivers. We have estimated destination choice models with a multinomial logit and with a nested logit structure. It was found that passenger demand at the pickup area, hotspot locations, service location preference, and major transport hubs positively influence a taxi driver's next choice of passenger pickup location. Results of this study provide insight regarding the factors that explain a taxi driver's probability to choose a certain zone within a set of passenger pickup zones, contributing to a better understanding of taxi drivers travel behaviour.

Dynamic Graph Convolutional Network-Based Prediction of the Urban Grid-Level Taxi Demand–Supply Imbalance Using GPS Trajectories

“…The increasing use of GPS devices in modern cities has led to the emergence of intelligent applications aimed at improving or transforming taxi services [32]. For instance, modeling the location choice of taxi drivers for passenger pickup using GPS data [33], discussing how informal transport can be better understood using GPS tracking data [34], modeling the spatial-temporal of yellow taxi demands in New York City using generalized STAR models [35], etc. With the support of data, this sentence highlights the potential for data-driven methodologies in the field of taxi services.…”

Exploring Built Environment Influence on Taxi Vacant Time in Megacities: A Case Study of Chongqing, China

“…For example, most drivers prefer to go to hotspots such as commercial areas, CBDs (Central Business Districts), and transportation hubs to search for passengers, where they can get passengers faster, thus reducing search time and increasing income [24]. Meanwhile, it was found that passenger demand at the pick-up area, hotspot locations, service location preference, and major transport hubs positively influence a taxi driver's next choice of passenger pick-up location [25]. Different regional or location preferences lead to differences in the probability or duration of taxi presence in the different built environment and thus affect the taxi vacant time under different built environments.…”

Optimal Routing with Spatial-Temporal Dependencies for Traffic Flow Control in Intelligent Transportation Systems

“…In many cities, however, people who want to demand a taxi are accustomed to parking an empty taxi by "roadside beckoning". As a result, the areas where taxi drivers receive their passengers are exceptionally arbitrary [3,4]. Both those who want to get a taxi and taxi drivers do not have enough data about a better area, which makes it surprising that a taxi driver experiences difficulties in locating passengers, while people think it is not easy to find an empty taxi.…”